A mayor's innovation office lists predictive maintenance, citizen sentiment analysis, and dynamic routing as candidate pilots. What Big Data rationale best justifies treating them as application classes?
Select an answer to reveal the explanation.
Short Explanation
Predictive maintenance, sentiment, and routing all chew through messy, multi-source evidence that a lonely weekly PDF cannot. That is why they sit in the Big Data applications family—insight from complex feeds, not "delete history" theater. Keep the relational systems that still fit; add Big Data patterns where the workload outgrows them.
Full Explanation
Typical Big Data application classes—such as predictive maintenance, sentiment analysis, and routing optimization—fit because they extract operational insight from large, complex, or multi-source data beyond conventional single-system reporting. They do not require erasing history or abolishing every RDBMS, nor are they equivalent to static weekly PDFs. The innovation office should justify pilots by workload fit to Big Data capabilities, not by absolutist tool bans.